{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from meta import Meta\n",
    "%pylab inline\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'data/test.tfrecords'\n",
    "filename_queue = tf.train.string_input_producer([filename], num_epochs=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = tf.TFRecordReader()\n",
    "_, serialized_example = reader.read(filename_queue)\n",
    "features = tf.parse_single_example(\n",
    "  serialized_example,\n",
    "  features={\n",
    "      'image': tf.FixedLenFeature([], tf.string),\n",
    "      'length': tf.FixedLenFeature([], tf.int64),\n",
    "      'digits': tf.FixedLenFeature([5], tf.int64)\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = tf.decode_raw(features['image'], tf.uint8)\n",
    "image = tf.reshape(image, [64, 64, 3])\n",
    "length = features['length']\n",
    "digits = features['digits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "coord = tf.train.Coordinator()\n",
    "threads = tf.train.start_queue_runners(coord=coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length: 1, digits: 4,0,0,0,0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb4483246d0>"
      ]
     },
     "execution_count": 6,
     "output_type": "execute_result",
     "metadata": {}
    },
    {
     "data": {
      "image/png": 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RTy0p7R5cYG21N2dUrsYScsLT7TBs+XZ08VRXM+GOAgGF2InEVwghVoDEV7ij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\nhBBCCCHWg/8POZHoLObUhzoAAAAASUVORK5CYII=\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "(image_val, length_val, digits_val) = sess.run([image, length, digits])\n",
    "\n",
    "print 'length: %d, digits: %d,%d,%d,%d,%d' % (\n",
    "    length_val, digits_val[0], digits_val[1], digits_val[2], digits_val[3], digits_val[4])\n",
    "\n",
    "imshow(image_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_train_examples: 30101\n",
      "num_val_examples: 3300\n",
      "num_test_examples: 13068\n"
     ]
    }
   ],
   "source": [
    "meta = Meta()\n",
    "meta.load('data/meta.json')\n",
    "print 'num_train_examples: %d' % meta.num_train_examples\n",
    "print 'num_val_examples: %d' % meta.num_val_examples\n",
    "print 'num_test_examples: %d' % meta.num_test_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "coord.request_stop()\n",
    "coord.join(threads)\n",
    "sess.close()"
   ]
  }
 ],
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